Revisiting Multimodal Positional Encoding in Vision-Language Models

Jie Huang, XuejingLiu, Sibo Song, Ruibing Hou, Hong Chang, Junyang Lin, Shuai Bai

Revisiting Multimodal Positional Encoding in Vision-Language Models: 23 upvotes on Hugging Face Daily Papers, #9 of 23 papers on 2025-11-03. Day-by-day upvote history.

Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation. Through extensive experiments, we identify three key guidelines: positional coherence, full frequency utilization, and preservation of textual priors-ensuring unambiguous layout, rich representation, and faithful transfer from the pre-trained LLM. Based on these insights, we propose Multi-Head RoPE (MHRoPE) and MRoPE-Interleave (MRoPE-I), two simple and plug-and-play variants that require no architectural changes. Our methods consistently outperform existing approaches across diverse benchmarks, with significant improvements in both general and fine-grained multimodal understanding. Code will be avaliable at https://github.com/JJJYmmm/Multimodal-RoPEs.

Paper page on Hugging Face · arXiv

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